System and method for classification of coronary artery disease based on metadata and cardiovascular signals

ABSTRACT

Non-invasive methods for accurately classifying Coronary Artery Disease (CAD) is a challenging task. In the present disclosure, a two stage classification is performed. In the first stage of classification, a metadata based rule engine is utilized to classify a subject into one of a confirmed CAD subject, a CAD subject and a non-CAD subject. Here, a set of optimal parameters are selected from a set of metadata associated with the subject based on a difference in frequency of occurrence of the CAD among a disease population and a non-disease population. Further, an optimal threshold associated with each optimal parameter is calculated based on an inflexion based correlation analysis. Further, the CAD subject, classified by the metadata based rule engine is further reclassified in a second stage by utilizing a set of cardiovascular signal into one of the CAD subject and the non-CAD subject.

PRIORITY CLAIM

This U.S. patent application claims priority under 35 U.S.C. § 119 to:India Application No. 201821009796, filed on Mar. 16, 2018. The entirecontents of the aforementioned application are incorporated herein byreference.

TECHNICAL FIELD

The embodiments herein generally relates, in general, to healthmonitoring and, in particular, to a system and method for classificationof Coronary Artery Disease (CAD) based on metadata and cardiovascularsignals.

BACKGROUND

A Coronary Artery Disease (CAD), is a common cardiovascular diseaseaffecting millions of people every year. The CAD typically occurs due todeposition of cholesterol and other fatty materials over time on theinner wall of a coronary artery. The deposition causes gradual loss ofnatural elastic property of the coronary artery and thereby restrictingthe free flow of blood in the coronary artery. The restriction of freeflow of blood in the coronary artery causes chest pain (angina) andheart attack.

Typically, a Coronary Angiogram (CAG) is considered as a gold standardtechnique for clinically identifying the CAD along with the level ofheart blockage. However, the CAG is an invasive procedure and isassociated with mortality risk. Additionally, the CAG is not freelyavailable and requires a modern hospital set-up to carry out. Moreover,the CAG may not detect an onset of CAD and there is a challenge in usingthe CAG as a mass screening system for detecting CAD. Moreover, theconventional methods for identifying the CAD are utilizing multiplecardiovascular biomedical signals including heart sound orphonocardiogram (PCG), electrocardiogram (ECG), photoplethysmogram (PPG)and the like. Moreover, demography, life style, self and family medicalhistory of a subject also play important roles to estimate cardiac riskfactor of the subject. Hence, there is a challenge in developing amethod for an early non-invasive and accurate detection of the CAD.

SUMMARY

Embodiments of the present disclosure present technological improvementsas solutions to one or more of the above-mentioned technical problemsrecognized by the inventors in conventional systems. For example, in oneembodiment, a method for classification of Coronary Artery Disease (CAD)based on metadata and cardiovascular signals is provided. The methodincludes receiving, a set of metadata associated with a subject and aset of cardiovascular signal associated with the subject, wherein eachmetadata from the set of metadata is associated with a value, by the oneor more hardware processors. Further, the method includes selecting, aset of optimal parameters from the set of metadata based on a differencein frequency of occurrence of CAD among a disease population and anon-disease population, by the one or more hardware processors.Furthermore, the method includes computing, an optimal threshold foreach optimal parameter from the set of optimal parameters based on aninflexion point based correlation analysis, by the one or more hardwareprocessors. Furthermore, the method includes classifying, the subject byutilizing a metadata based rule engine into a category among a pluralityof categories, wherein the plurality of categories comprising aconfirmed CAD subject, a CAD subject and a non-CAD subject, wherein themetadata based rule engine is constructed by utilizing the set ofoptimal parameters and the optimal threshold associated with eachoptimal parameter, by the one or more hardware processors. Furthermore,the method includes reclassifying, the CAD subject into one of theconfirmed CAD subject and the non-CAD subject based on a combination ofa PPG signal classifier and a PCG signal classifier in parallel, by theone or more hardware processors.

In another aspect, a system for classification of CAD based on metadataand cardiovascular signals is provided. The system includes one or morememories comprising programmed instructions and a repository for storinga set of metadata associated with a subject and a set of cardiovascularsignal associated with the subject, one or more hardware processorsoperatively coupled to the one or more memories, wherein the one or morehardware processors are capable of executing the programmed instructionsstored in the one or more memories, a PPG signal capturing unit, a PCGsignal capturing unit and a CAD analysis unit, wherein the CAD analysisunit is configured to receive, a set of metadata associated with asubject and a set of cardiovascular signal associated with the subject,wherein each metadata from the set of meta data is associated with avalue. Further, the CAD analysis unit is configured to select, a set ofoptimal parameters from the set of metadata based on a difference infrequency of occurrence of CAD among a disease population and anon-disease population. Furthermore the CAD analysis unit is configuredto compute, an optimal threshold for each optimal parameter from the setof optimal parameters based on an inflexion point based correlationanalysis. Furthermore, the CAD analysis unit is configured to classify,the subject by utilizing a metadata based rule engine into a categoryamong a plurality of categories, wherein the plurality of categoriescomprising a confirmed CAD subject, a CAD subject and a non-CAD subject,wherein the metadata based rule engine is constructed by utilizing theset of optimal parameters and the optimal threshold associated with eachoptimal parameter. Furthermore, the CAD analysis unit is configured toreclassify, the CAD subject into one of the confirmed CAD subject andthe non-CAD subject based on a combination of a PPG signal classifierand a PCG signal classifier in parallel.

In yet another aspect, a computer program product comprising anon-transitory computer-readable medium having embodied therein acomputer program for system and method for classification of CAD basedon metadata and cardiovascular signals, is provided. The computerreadable program, when executed on a computing device, causes thecomputing device to receive, a set of metadata associated with a subjectand a set of cardiovascular signal associated with the subject, whereineach metadata from the set of metadata is associated with a value.Further, the computer readable program, when executed on a computingdevice, causes the computing device to select, a set of optimalparameters from the set of metadata based on a difference in frequencyof occurrence of CAD among a disease population and a non-diseasepopulation. Furthermore, the computer readable program, when executed ona computing device, causes the computing device to compute, an optimalthreshold for each optimal parameter from the set of optimal parametersbased on an inflexion point based correlation analysis. Furthermore, thecomputer readable program, when executed on a computing device, causesthe computing device to classify, the subject by utilizing a metadatabased rule engine into a category among a plurality of categories,wherein the plurality of categories comprising a confirmed CAD subject,a CAD subject and a non-CAD subject, wherein the metadata based ruleengine is constructed by utilizing the set of optimal parameters and theoptimal threshold associated with each optimal parameter. Furthermore,the computer readable program, when executed on a computing device,causes the computing device to reclassify, the CAD subject into one ofthe confirmed CAD subject and the non-CAD subject based on a combinationof a PPG signal classifier and a PCG signal classifier in parallel.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate exemplary embodiments and, togetherwith the description, serve to explain the disclosed principles:

FIG. 1 illustrates a network environment implementing a system andmethod for classification of Coronary Artery Disease (CAD) based onmetadata and cardiovascular signals, according to some embodiments ofthe present disclosure;

FIG. 2 illustrates a block diagram of the system for classification ofCAD based on metadata and cardiovascular signals, according to someembodiments of the present disclosure;

FIG. 3 depicts an example architecture for CAD classification, accordingto some embodiments of the present disclosure;

FIG. 4A depicts an example histogram associated with a diseasepopulation for age parameter, according to some embodiments of thepresent disclosure;

FIG. 4B depicts an example histogram associated with a non-diseasepopulation for age parameter, according to some embodiments of thepresent disclosure;

FIG. 4C depicts an example bar diagram, comparing a number of diabeticsubjects and a number of non-diabetic subjects in the disease populationand in the non-disease population, according to some embodiments of thepresent disclosure;

FIG. 4D depicts an example bar diagram comparing a number of smokingsubjects and a number of non-smoking subjects in the disease populationand in the non-disease population, according to some embodiments of thepresent disclosure;

FIG. 4E depicts an example two dimensional plot illustrating a set ofcorrelation coefficients between the disease population and thenon-disease population corresponding to the age parameter, according tosome embodiments of the present disclosure;

FIG. 5 depicts an example metadata based rule engine, according to someembodiments of the present disclosure;

FIG. 6A depicts an example architecture for cardiovascular signal basedreclassification, according to some embodiments of the presentdisclosure;

FIG. 6B depicts an example Power Spectral Density (PSD) plot of HeartRate Variability (HRV) signals, for a sample CAD subject, according tosome embodiments of the present disclosure;

FIG. 6C depicts an example Power Spectral Density (PSD) plot of HRVsignals, for a non-CAD subject, according to some embodiments of thepresent disclosure;

FIG. 6D depicts an example PPG signal with the set of PPG signalparameters, according to some embodiments of the present disclosure;

FIG. 7 illustrates a flow diagram for classification of the CAD based onmetadata and cardiovascular signals, according to some embodiments ofthe present disclosure;

FIG. 8 illustrates a flow diagram for identifying an optimal thresholdfor each optimal parameter from a set of optimal parameters based on aninflexion point based correlation analysis, according to someembodiments of the present disclosure;

FIG. 9 illustrates a flow diagram for reclassifying a CAD subject basedon a combination of the PPG signal classifier and the PCG signalclassifier in parallel, according to some embodiments of the presentdisclosure; and

FIG. 10 illustrates a comparative study of individual PCG and PPGclassifiers, the metadata based rule engine, a fusion classifier and atwo-stage classifier in terms of mean sensitivity and specificity,according to some embodiments of the present disclosure.

It should be appreciated by those skilled in the art that any blockdiagrams herein represent conceptual views of illustrative systems anddevices embodying the principles of the present subject matter.Similarly, it will be appreciated that any flow charts, flow diagrams,and the like represent various processes which may be substantiallyrepresented in computer readable medium and so executed by a computer orprocessor, whether or not such computer or processor is explicitlyshown.

DETAILED DESCRIPTION

Exemplary embodiments are described with reference to the accompanyingdrawings. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears.Wherever convenient, the same reference numbers are used throughout thedrawings to refer to the same or like parts. While examples and featuresof disclosed principles are described herein, modifications,adaptations, and other implementations are possible without departingfrom the spirit and scope of the disclosed embodiments. It is intendedthat the following detailed description be considered as exemplary only,with the true scope and spirit being indicated by the following claims.

The present subject matter overcomes the limitations of the conventionalCoronary Artery Detection (CAD) methods based on a non-invasive twostage classification approach. In the first stage of classification, ametadata based rule engine is utilized for classifying a subject as aconfirmed CAD subject, a CAD subject and a non-CAD subject. Here, themetadata includes a set of demographic data associated with the subject,a set of clinical information associated with the subject and a medicalhistory associated with the subject. The set of demographic dataincludes gender, age, weight and height. The set of clinical informationincludes Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP),Body Mass Index (BMI) and lipid profile. The medical history includessmoking history, diabetes history, hypertension, chest pain, medicationhistory of aspirin and statin, family history of diabetes, cardiacarrest and the like. The stage 1 classification can lead to aclassification error associated with the CAD subject. The classificationerror associated with the CAD subject is further rectified byreclassifying the CAD subject by utilizing a second stage ofclassification based on a set of cardio vascular signals. In anembodiment, the set of cardiovascular signals includes aPhotoplethysmogram (PPG) signal and a Phonocardiogram (PCG) signal. Animplementation of the system and method for classification of CoronaryArtery Disease (CAD) based on metadata and cardiovascular signals isdescribed further in detail with reference to FIGS. 1 through 10.

Referring now to the drawings, and more particularly to FIGS. 1 through10, where similar reference characters denote corresponding featuresconsistently throughout the figures, there are shown preferredembodiments and these embodiments are described in the context of thefollowing exemplary system and/or method.

FIG. 1 illustrates a network environment 100 implementing a system 102for classification of CAD based on metadata and cardiovascular signals,according to some embodiments of the present disclosure. The system forclassification of CAD based on metadata and cardiovascular signals 102,hereinafter referred to as the system 102, is configured for receivingthe set of metadata associated with a subject and the set ofcardiovascular signals associated with the subject. In an embodiment,the cardiovascular signals includes the PPG signal and the PCG signal.The PPG signal associated with the subject is recorded by utilizing thedevice 120. In an embodiment, the device 120 can be a commerciallyavailable pulse oximeter. The PCG signal associated with the subject isrecorded by utilizing the device 130. In an embodiment, the device 130can be a low-cost digital stethoscope. The system 102 may be embodied ina computing device, for instance a computing device 104.

Although the present disclosure is explained considering that the system102 is implemented on a server, it may be understood that the system 102may also be implemented in a variety of computing systems, such as alaptop computer, a desktop computer, a notebook, a workstation, asmartphone, a cloud-based computing environment and the like. In oneimplementation, the system 102 may be implemented in a cloud-basedenvironment. It will be understood that the system 102 may be accessedby multiple users through one or more user devices 106-1, 106-2 . . .106-N, collectively referred to as user devices 106 hereinafter, orapplications residing on the user devices 106. Examples of the userdevices 106 may include, but are not limited to, a portable computer, apersonal digital assistant, a handheld device, a Smartphone, a TabletComputer, a workstation and the like. The user devices 106 arecommunicatively coupled to the system 102 through a network 108.

In an embodiment, the network 108 may be a wireless or a wired network,or a combination thereof. In an example, the network 108 can beimplemented as a computer network, as one of the different types ofnetworks, such as virtual private network (VPN), intranet, local areanetwork (LAN), wide area network (WAN), the internet, and such. Thenetwork 108 may either be a dedicated network or a shared network, whichrepresents an association of the different types of networks that use avariety of protocols, for example, Hypertext Transfer Protocol (HTTP),Transmission Control Protocol/Internet Protocol (TCP/IP), and WirelessApplication Protocol (WAP), to communicate with each other. Further, thenetwork 108 may include a variety of network devices, including routers,bridges, servers, computing devices, storage devices. The networkdevices within the network 108 may interact with the system 102 throughcommunication links.

As discussed above, the system 102 may be implemented in a computingdevice 104, such as a hand-held device, a laptop or other portablecomputer, a tablet computer, a mobile phone, a PDA, a smartphone, and adesktop computer. The system 102 may also be implemented in aworkstation, a mainframe computer, a server, and a network server. In anembodiment, the system 102 may be coupled to a data repository, forexample, a repository 112. The repository 112 may store data processed,received, and generated by the system 102. In an alternate embodiment,the system 102 may include the data repository 112. The components andfunctionalities of the system 102 are described further in detail withreference to FIG. 2.

FIG. 2 illustrates a block diagram of the system for classification ofCAD based on metadata and cardiovascular signals, according to someembodiments of the present disclosure. The system for classification ofCAD based on metadata and cardiovascular signals, 200 (hereinafterreferred to as system 200) may be an example of the system 102 (FIG. 1).In an example embodiment, the system 200 may be embodied in, or is indirect communication with the system, for example the system 102 (FIG.1). The system 200 includes or is otherwise in communication with one ormore hardware processors such as a processor 202, at least one memorysuch as a memory 204, an I/O interface 206 and a CAD analysis unit 250.In an embodiment, the CAD analysis unit 250 can be implemented as astandalone unit in the system 200 comprising an optimal thresholdcomputation module (not shown in FIG. 2), an optimal parameter selectionmodule (not shown in FIG. 2), a metadata based classification module(not shown in FIG. 2) and a cardiovascular signal based reclassificationmodule (not shown in FIG. 2). In another embodiment, the CAD analysisunit 250 can be implemented as a module in the memory 204 comprising theoptimal threshold computation module (not shown in FIG. 2), the optimalparameter selection module (not shown in FIG. 2), the metadata basedclassification module (not shown in FIG. 2) and the cardiovascularsignal based reclassification module (not shown in FIG. 2). Theprocessor 202, memory 204, and the I/O interface 206 may be coupled by asystem bus such as a system bus 208 or a similar mechanism.

The I/O interface 206 may include a variety of software and hardwareinterfaces, for example, a web interface, a graphical user interface,and the like. The interfaces 206 may include a variety of software andhardware interfaces, for example, interfaces for peripheral device(s),such as a keyboard, a mouse, an external memory, a camera device, and aprinter. Further, the interfaces 206 may enable the system 102 tocommunicate with other devices, such as web servers and externaldatabases. The interfaces 206 can facilitate multiple communicationswithin a wide variety of networks and protocol types, including wirednetworks, for example, local area network (LAN), cable, etc., andwireless networks, such as Wireless LAN (WLAN), cellular, or satellite.For the purpose, the interfaces 206 may include one or more ports forconnecting a number of computing systems with one another or to anotherserver computer. The I/O interface 206 may include one or more ports forconnecting a number of devices to one another or to another server.

The hardware processor 202 may be implemented as one or moremicroprocessors, microcomputers, microcontrollers, digital signalprocessors, central processing units, state machines, logic circuitries,and/or any devices that manipulate signals based on operationalinstructions. Among other capabilities, the hardware processor 202 isconfigured to fetch and execute computer-readable instructions stored inthe memory 204.

The memory 204 may include any computer-readable medium known in the artincluding, for example, volatile memory, such as static random accessmemory (SRAM) and dynamic random access memory (DRAM), and/ornon-volatile memory, such as read only memory (ROM), erasableprogrammable ROM, flash memories, hard disks, optical disks, andmagnetic tapes. In an embodiment, the memory 204 includes a plurality ofmodules 220 and a repository 240 for storing data processed, received,and generated by one or more of the modules 220 and the CAD analysisunit 250. The modules 220 may include routines, programs, objects,components, data structures, and so on, which perform particular tasksor implement particular abstract data types.

The memory 204 also includes module(s) 220 and a data repository 240.The module(s) 220 include programs or coded instructions that supplementapplications or functions performed by the system 200 for classificationof CAD based on metadata and cardiovascular signals. The modules 220,amongst other things, can include routines, programs, objects,components, and data structures, which perform particular tasks orimplement particular abstract data types. The modules 220 may also beused as, signal processor(s), state machine(s), logic circuitries,and/or any other device or component that manipulates signals based onoperational instructions. Further, the modules 220 can be used byhardware, by computer-readable instructions executed by a processingunit, or by a combination thereof. The modules 220 can include varioussub-modules (not shown). The modules 220 may include computer-readableinstructions that supplement applications or functions performed by thesystem 200 for classification of CAD based on metadata andcardiovascular signals.

The data repository 240 may include received set of metadata 242, thePPG signal data 244, the PCG signal data 246 and other data 248.Further, the other data 248 amongst other things, may serve as arepository for storing data that is processed, received, or generated asa result of the execution of one or more modules in the module(s) 220and the modules associated with the CAD analysis unit 250. Therepository 240 is further configured to maintain a plurality ofparameters associated with the set of metadata, the PPG signal data andthe PCG signal data stored in the data repository 240.

Although the data repository 240 is shown internal to the system 200 forclassification of CAD based on metadata and cardiovascular signals, itwill be noted that, in alternate embodiments, the data repository 240can also be implemented external to the system 200 for classification ofCAD based on metadata and cardiovascular signals, where the datarepository 240 may be stored within a database (not shown in FIG. 2)communicatively coupled to the system 200 for classification of CADbased on metadata and cardiovascular signals. The data contained withinsuch external database may be periodically updated. For example, newdata may be added into the database (not shown in FIG. 2) and/orexisting data may be modified and/or non-useful data may be deleted fromthe database (not shown in FIG. 2). In one example, the data may bestored in an external system, such as a Lightweight Directory AccessProtocol (LDAP) directory and a Relational Database Management System(RDBMS). In another embodiment, the data stored in the data repository240 may be distributed between the system 200 for classification of CADbased on metadata and cardiovascular signals and the external database.

FIG. 3 depicts an example architecture for CAD classification, accordingto some embodiments of the present disclosure. Now referring to FIG. 3,the set of metadata associated with the subject are received by themetadata based classification module (stage 1) 302. Here, a set ofoptimal parameters are chosen from the set of metadata, based on adifference in frequency of occurrence of CAD among a disease populationand a non-disease population. Further, an optimal threshold associatedwith each parameter from the set of optimal parameters is obtained byutilizing an inflexion point based correlation analysis. Further, a setof rules are created by utilizing the set of optimal parameters and theoptimal threshold associated with each optimal parameter. Further, themetadata based rule engine is constructed by utilizing the set of rulesand the subject is classified into one of the confirmed CAD subject, theCAD subject and the non-CAD subject. Here, the CAD subject undergoes areclassification (stage 2) 304 and the reclassification is based on thePPG signal parameters and the PCG signal parameters. Here, a set of PPGsignal parameters are extracted from the PPG signal and a set of PCGsignal parameters are extracted from the PCG signal in parallel.Further, the set of PPG signal parameters are classified by utilizing apre-trained PPG signal classifier to obtain a first confidence score andthe set of PCG signal parameters are classified by utilizing apre-trained PCG signal classifier to obtain a second confidence score.Further, the first confidence score and the second confidence score arecompared and a decision associated with the highest score among thefirst confidence score and the second confidence score is utilized toclassify the CAD subject into one of the confirmed CAD subject and thenon-CAD subject. In an embodiment, the reclassification of the subjectas one of the confirmed CAD subject and the non-CAD subject is displayedon the smart phone.

The CAD analysis unit 250 of the CAD classification system 200 can beconfigured to receive the set of metadata associated with the subjectand the set of cardiovascular signals associated with the subject,wherein each metadata from the set of metadata is associated with avalue. Here, the metadata includes the set of demographic dataassociated with the subject, the set of clinical information associatedwith the subject and the medical history associated with the subject.The set of demographic data includes gender, age, weight and height. Theset of clinical information includes Systolic Blood Pressure (SBP),Diastolic Blood Pressure (DBP), Body Mass Index (BMI) and lipid profile.Here, MAP=⅓×SBP+⅔×DBP. The medical history includes smoking history,diabetes history, hypertension, chest pain, medication history ofaspirin and statin, family history of diabetes, cardiac arrest and thelike. In an embodiment, the cardiovascular signals includes the PPGsignal and the PCG signal. In an embodiment, the PPG signal associatedwith the CAD subject is recorded from the right hand index finger byutilizing a commercially available non-medical grade fingertip CONTEC™Medical Systems (CMS) 50D+ pulse-oximeter at a sampling rate of 60 Hz.The CMS 50D+ pulse-oximeter is connected to the smart phone via aUniversal Serial Bus-On-The-Go (USB-OTG) cable or via Bluetooth and iscapable of storing the recorded PPG signal via USB interface for offlineprocessing. Further, a signal quality assessment algorithm based on PPGmorphology is applied to extract 2 minutes of good quality PPG signalfrom the subject for further processing. The duration of recordingensures to preserve the Heart Rate Variability (HRV) related informationin the collected signal and also a low computation time for sending therecorded data to server, feature extraction and classification. Inparallel, the PCG signal associated with the CAD subject is recorded byutilizing a low cost in-house digital stethoscope. The low cost in-housedigital stethoscope is capable of storing multiple PCG signal recordingssimultaneously and is connected to the smart phone via 3.5 mm audiojack. Here, the PCG signal is recorded for 30 seconds and the quality ofthe PCG signal is assessed by utilizing a PCG signal quality assessmentmodule associated with the smart phone. Here, the audio sampling rate ofthe PCG signal is predefined at 8000 Hz and the PCG signals are recordedfrom left third intercostal space of the subject. The subject is insupine position during the recording of the PCG signal.

Further, CAD analysis unit 250 of the CAD classification system can beconfigured to select a set of optimal parameters from the set ofmetadata based on a difference in frequency of occurrence of CAD amongthe disease population and the non-disease population. In an embodiment,seven metadata are selected as optimal parameters. The seven optimalparameters includes age, diabetes, Maximum Arterial Pressure (MAP),hypertension, family cardiac history, BMI and chest pain. From medicaldomain, it is evident that if a subject belongs to a relatively high agegroup and is of diabetic then he/she has high probability of gettingCAD. In an embodiment, the method of selecting the set of optimalparameters from the set of metadata based on a difference in frequencyof occurrence of CAD among the disease population and the non-diseasepopulation is explained with reference to FIG. 4A to 4D. FIG. 4A depictsan example histogram associated with the disease population for ageparameter, according to some embodiments of the present disclosure. Nowreferring to FIG. 4A, age of the subject under the disease population isplotted along an X plane and a frequency of occurrence of the CAD amongthe disease population is plotted along a Y plane. Here, the frequencyof occurrence of the CAD disease under the disease population increasesas the age associated with the disease population increases. FIG. 4Bdepicts an example histogram associated with the control population forage parameter, according to some embodiments of the present disclosure.Now referring to FIG. 4B, age of the subject under the non-diseasepopulation is plotted along an X plane and a frequency of occurrence ofthe CAD among the non-disease population is plotted along a Y plane.Here, the frequency of occurrence of the CAD disease under thenon-disease population has less impact on the age associated with thenon-disease population increases. Hence, the heuristic, “age has asignificant relation with the CAD” is verified based on FIG. 4A and FIG.4B. The said heuristic can be supported medically, as older people startlosing natural elastic properties of arteries, causing narrowing ofcoronary artery.

FIG. 4C depicts an example bar diagram, comparing a number of diabeticsubjects and a number of non-diabetic subjects in the disease populationand in the non-disease population, according to some embodiments of thepresent disclosure. Now, referring to FIG. 4C, the Y plane indicates theage associated with the disease population and the non-diseasepopulation. Here, a histogram 402 indicates the number of diabeticsubjects in the disease population, a histogram 404 indicates the numberof non-diabetic subjects in the disease population, a histogram 406indicates the number of diabetic subjects in the non-disease populationand a histogram 408 indicates the number of non-diabetic subjects in thenon-disease population. Here, while comparing the number of diabeticsubjects 402 in the disease population and the number of diabeticsubjects 406 in the non-disease population, the number of diabeticsubjects are more in the disease population than the number of diabeticsubjects in the non-disease population. Hence, the subject above 40years of age and with diabetics are having high probability to have CAD.

FIG. 4D depicts an example bar diagram comparing a number of smokingsubjects and a number of non-smoking subjects in the disease populationand in the non-disease population, according to some embodiments of thepresent disclosure. Now, referring to FIG. 4D, the Y plane indicates theage associated with the disease population and the non-diseasepopulation. Here, a histogram 410 indicates the number of smokingsubjects in the disease population, a histogram 412 indicates the numberof non-smoking subjects in the disease population, a histogram 414indicates the number of smoking subjects in the non-disease populationand a histogram 416 indicates the number of non-smoking subjects in thenon-disease population. Here, while comparing the number of smokingsubjects 410 in the disease population and the number of smokingsubjects 414 in the non-disease population, the number of smokingsubjects are more in the disease population than the number of smokingsubjects in the non-disease population. Hence, the subject above 40years of age and with smoking habit are having high probability to haveCAD. Similarly, the set of optimal parameters are selected from the setof metadata based on the difference in frequency of occurrence of CADamong the disease population and the non-disease population.

Further, CAD analysis unit 250 of the CAD classification system can befurther configured to identify an optimal threshold for each metadatafrom the set of metadata based on the inflexion point based correlationanalysis. Here, the inflexion point based correlation analysis isperformed as follows: (i) Select an age point T and remove all subjectsof age less than or equal to T to obtain a set of subjects. (ii)calculate a correlation coefficient (r) between a histogram associatedwith the disease population and a histogram associated with thenon-disease population by utilizing Bhattacharya distance. (iii)construct an XY plot, wherein the optimal parameter associated with thesubject is plotted along an X plane and the correlation coefficient isplotted along a Y plane. (iv) compare each point in the XY plot with theprevious point to identify any decrease or increase in correlationcoefficient (r) from the previous point. If a highest inflexion occursbetween the present point and the previous point, select the previouspoint as the optimum threshold value. FIG. 4E depicts an example twodimensional plot illustrating a set of correlation coefficients betweenthe disease population and the non-disease population corresponding tothe age parameter, according to some embodiments of the presentdisclosure. Now, referring to FIG. 4E, age of the subject is plottedalong an X plane and the correlation coefficient is plotted along a Yplane. Here, the highest inflexion occurs between the point 420 (age=45)and the point 422. Since the highest inflexion occurred at age 45,age=45 is selected as the optimal threshold. In the same way, theoptimal threshold associated with each optimal parameter from the set ofoptimal parameters are calculated by utilizing the inflexion point basedcorrelation analysis.

Further the CAD analysis unit 250 of the CAD classification system canbe configured to classify the subject by utilizing the metadata basedrule engine into a category among a plurality of categories, wherein theplurality of categories comprising the confirmed CAD subject, the CADsubject and the non-CAD subject, wherein the metadata based rule engineis constructed by utilizing the set of optimal parameters and theoptimal threshold associated with each optimal parameter. FIG. 5 depictsan example metadata based rule engine, according to some embodiments ofthe present disclosure. Now, referring to FIG. 5, at step 502, thesubject is tested for age, diabetic and chest pain. If the age is morethan 45 years, if the subject is diabetic, and if the subject is withchest pain, the subject is classified as the confirmed CAD subject bythe metadata based rule engine. If the above condition at step 502 isnot satisfied, the MAP of the subject is tested at step 504 and if theMAP is greater than 100, the subject is classified as the CAD subject.If the MAP is less than or equal to 100, the subject is tested for ageand cardiac activity at step 506. If the age is greater than 40 yearsand if the subject is having family cardiac history, then the subject isclassified as the CAD subject. Otherwise, age and BMI associated withthe subject is tested at step 508. If the age is greater than 40 and BMIis greater than 23, then the subject is classified as the CAD subject.Otherwise, the subject is tested for age and hypertension at step 510.If the age of the subject is greater than 40 and the subject is havinghypertension, then the subject is classified as the CAD subject.Otherwise, the subject is tested for age and chest pain at step 512. Ifthe age of the subject is greater than 30 and the subject is havingchest pain, then the subject is classified as the CAD subject.Otherwise, the subject is classified as the non-CAD subject. In anembodiment, the metadata based rule engine is trained with a 5-foldcross validation data. Additionally, the 5-fold cross validation data isutilized for tuning the hyper parameters associated with the metadatabased rule engine.

Further, the CAD analysis unit 250 of the CAD classification system canbe configured to reclassify, the CAD subject into one of the confirmedCAD subject and the non-CAD subject based on a combination of the PPGsignal classifier and the PCG signal classifier in parallel. FIG. 6Adepicts an example architecture for cardiovascular signal basedreclassification, according to some embodiments of the presentdisclosure. Now, referring to FIG. 6A, the PPG signal and the PCG signalassociated with the CAD subject is received and processed as follows:the PPG signal measures a volumetric blood flow in capillaries and afundamental frequency of the PPG signal indicates a heart rateassociated with the subject. The PPG signal captured by utilizing thepulse-oximeter may be associated with a noise. At step 602, the noiseassociated with the PPG signal is removed. Here, the PPG signalassociated with the noise is fed into a Butterworth low pass filter witha cut-off frequencies of 0.5 Hz and 10 Hz to remove a plurality ofundesired frequency components. Further, a set of PPG signal parametersare extracted at step 604. In parallel, a noise associated with the PCGsignal is removed at step 608 and a set of PCG signal parameters areextracted from the PCG signal at step 610. At step 606, the set of PPGsignal parameters are classified by utilizing a pre-trained PPG signalclassifier to obtain a first confidence score. At step 612, the set ofPCG signal parameters are classified by utilizing a pre-trained PCGsignal classifier to obtain a second confidence score in parallel. In anembodiment, a nonlinear Support Vector Machine (SVM) with Radial BasisFunction (RBF) kernel is used as the PPG and the PCG signal classifier.Here, the PPG signal classifier is pre-trained by utilizing a PPGtraining model 614 and the PCG signal classifier is pre-trained byutilizing a PCG training model 616. The SVM separates two classes in amultidimensional feature space by fitting an optimal separatinghyperplane (OSH) to the training samples. The objective function of SVMaims to maximize the margin between the hyperplane and the closesttraining samples, known as support vectors. Thus, for a given test datapoint, if the distance to the hyperplane is higher, then the outputclass label is more reliable. Further, the first confidence score fromthe PPG signal classifier and the second confidence score from the PCGsignal classifier are fused at step 618 to obtain a final classificationof the CAD subject into one among the CAD subject and the non-CADsubject. Here, a maximum confidence score is obtained by comparing thefirst confidence score and the second confidence score and the finalclassification of the subject is based on the maximum confidence score.For example, if the first confidence score is 0.78 for the confirmed CADsubject and 0.22 for the non-CAD subject. The second confidence score is0.45 for the confirmed CAD subject and 0.65 for the non-CAD subject, thesubject is decided as the confirmed CAD subject based on the firstconfidence score for the confirmed CAD subject.

In an embodiment, the set of PPG signal parameters includes a pluralityof morphological PPG features, a plurality of time domain PPG featuresand a plurality of frequency domain PPG features associated with the PPGsignal. Here, the plurality of morphological PPG features includes arising time, a falling time, a rising to falling tile ration and pulsewidth. In an embodiment, a plurality of morphological features andfrequency domain features are extracted from a spectrum of HRV signal asfollows: Here, the CAD subjects includes a lesser HRV signal compared tonon-CAD subjects. Here, a peak to peak interval distance time seriesobtained from the PPG signal is termed as HRV signal. For brevity ofdescription, the peak to peak interval distance time series isalternatively represented as NN interval (interval between normalpeaks). Initially, an unequal sampling rate of the HRV signal is set toa fixed sampling rate of 2 Hz using cubic spline technique. Further,spectral analysis is performed on the HRV signal based on Welch'salgorithm, to estimate the power spectrum of the HRV signal using anaveraging modified periodogram. Here, the power spectrum of the HRVsignal is divided into 3 bands, namely, very low frequency (V LF, 0-0.04Hz), low frequency (LF, 0.04-0.15 Hz) and high frequency (HF, 0.15-0.4Hz) regions. The normalized spectral power within the 3 frequency bands(nVLF, nLF, nHF) with respect to total spectral power are computed asfeatures. The LF region is typically considered as markers ofsympathetic modulation and the HF region contains the rhythms regulatedby parasympathetic activities. FIG. 6B depicts an example Power SpectralDensity (PSD) plot of HRV signals, for a sample CAD subject, accordingto some embodiments of the present disclosure. FIG. 6C depicts anexample Power Spectral Density (PSD) plot of HRV signals, for a samplenon-CAD subject, according to some embodiments of the presentdisclosure. Now, referring to FIG. 6B and FIG. 6C, the duration ofmeasurement is fixed for half a minute. It can be observed that thespectral power contents for all the 3 frequency regions (V LF, LF andHF) is much lesser for a CAD patient, due to the reduced HRV compared toa non-CAD subject. In an embodiment, the plurality of time domainfeatures associated with the PPG signal are extracted as follows: FIG.6D depicts an example PPG signal with the set of PPG signal parameters,according to some embodiments of the present disclosure. Now referringto FIG. 6D, a sample PPG signal indicating some of the commonly usedfeatures are shown. Each cycle of a PPG waveform includes two terminaltrough points and a peak corresponding to diastole and systole. Adiccrotic notch is located in between the peak and succeeding trough.The plurality of time domain PPG features includes (1) mean of pulsewidth (T_(c)) (2) standard deviation of pulse width (T_(c)) (3) mean ofrelative crest time (T₁=T_(s)/T_(c)), 4) standard deviation of relativecrest time (5) mean of relative diastolic time (T₂=T_(d)/T_(c)) (6)standard deviation of relative diastolic time (7) mean of time ratiobetween crest time and diastolic time (R=T_(s)/T_(d)) and (8) standarddeviation of R, calculated from every recording. Here, T_(s) is theCrest time, T_(d) is the Diastolic Time, T_(c) is the Pulse Width, pk2pkis the Peak to peak interval, B₃₃ is the Pulse width at 33% height andB₇₅ is the Pulse width at 75% height. Table 1 illustrates the set of PPGsignal parameters and a range of values associated with the CAD subjectand the non-CAD subject. Further, the set of PPG signal parameters areclassified by utilizing a pre-trained PPG signal classifier to obtain afirst confidence score. Here, the PPG signal classifier is pre-trainedby utilizing a PPG training model 614.

In an embodiment, the set of PCG signal parameters includes a pluralityof PCG morphological features, a plurality of time domain PCG featuresand a plurality of frequency domain PCG features. Here, the plurality ofPCG morphological features includes, a Systole_1 to Systole_2 duration,a spectral power, a systolic width, a diastolic width etc Here,fundamental heart sounds including a systolic region and a diastolicregion associated with each cardiac cycle are identified from the PCGsignal in a typical segmentation based approach. Here, a set of timedomain PCG features and a set of frequency domain PCG features areextracted from the segmented regions of the PCG signal. The segmentationbased approach requires an automatic segregation of the fundamentalheart sounds. Further a non-segmentation based approach is utilized forheart sound analysis. Further, a low pass filter is used to remove allthe frequency components above 500 Hz. The selection of the cut-offfrequency of the filter ensures to preserve the relevant informationregarding cardiac functionalities and removes all high frequency noisecomponents. In order to compute features corresponding to individualheartbeat, the entire recording is processed by splitting into smallwindows, using rectangular window having 50% overlapping. Since heartrate of a stable cardiac subject does not go below 30 BPM (Beats PerMinute), a window length of 2 seconds duration ensures the presence ofat least one complete heart beat in every window. The final feature setfor creating the classifier is selected based on Maximal InformationCoefficient (MIC). Further, a Short Time Fourier Transform (STFT)corresponding to every window of the PCG recording is computed to getthe spectrum, for extracting the set of frequency domain PCG features.For kth time window W_(k)(t), the amplitude of spectral power infrequency domain is denoted by S_(k)(ω) and N is the length of thewindow for expressing the set of frequency domain PCG features. Here,the set of PCG signal parameters includes a Ratio of spectral power, aspectral centroid, a spectral roll-off, a spectral flux, a kurtosis ofthe time signal in a window, a natural entropy and Tsallis entropy.Table 2 illustrates the set of PCG signal parameters and a range ofvalues associated with the CAD subject and the non-CAD subject.

Ratio of spectral power between 0-100 Hz and 100-150 Hz:(R=P₀₋₁₀₀/P₁₀₀₋₁₅₀). The frequency components present in the heart soundspectrum of the CAD patient above 100 Hz are significant and hence thenumerical value of the parameter R is typically found more for a non-CADsubject compared to the CAD subject.

Spectral centroid: The spectral centroid as given in equation 1indicates the frequency region, where most of the spectral energy isconverged. Since the number of frequency components are more above 100Hz for the CAD subject, the frequency centroid is shifted more towardsthe right-side of the spectrum.

cen=Σ_(ω=1) ^(N)ω*S_(k) ^((ω))/_(Σ) _(ω=1) ^(N)ω  (1)

Spectral roll-off: The spectral roll-off as given in equation 2 measuresthe region, containing 85% of the total spectral energy.

SR=0.85*Σ_(ω=1) ^(N) S _(k)(ω)  (2)

Spectral flux: The spectral flux as given on equation 3 measuresabsolute difference in spectral energy between two successive windows. Ahigher value of the spectral flux parameter indicates a rapidfluctuation in heart sounds in successive heart beats.

SF=(∥S _(k)(ω)−S _(k-1)(ω)∥)  (3)

The Natural entropy H (x) and the Tsallis entropy S_(q)(x) are as givenin equation 4 and equation 5.

$\begin{matrix}{{H(x)} = {- {\sum_{i}{{{prob}\left( x_{i} \right)}\mspace{14mu} {\ln \left( {p\left( x_{i} \right)} \right)}}}}} & (4) \\{{S_{q}(x)} = {\frac{k}{q - 1}\left( {1 - {\sum_{i}{{prob}\left( x_{i}^{q} \right)}}} \right)}} & (5)\end{matrix}$

Where, prob(x_(i)) is the probability of ith PCG sample, x_(i), k and qare real parameters equal to 1 and 2 respectively. Table 2 shows therange of different PCG features for CAD and non-CAD subjects obtainedfrom our dataset. It is to be noted that, the reported values of thefrequency features are calculated in terms of FFT points not in Hz. Itcan be observed that CAD patients typically exhibit higher values ofspectral centroid, spectral roll off but lower spectral power ratio. PCGsignals of CAD subjects often show more irregularities than non-CADsubjects, resulting in higher numerical values of spectral flux, naturaland Tsallis entropy.

TABLE 1 Ranges of PPG features for the CAD and the non-CAD subjects.Non-CAD Sl. CAD range range No PPG features Mean ± SD Mean ± SD 1.Spectral power of NN intervals 0.99 ± 0.3  1.31 ± 0.3  in 0-0.04 Hz 2.Spectral power of NN intervals 0.05 ± 0.02 0.08 ± 0.01 in 0.04-0.15 Hz3. Spectral power of NN intervals 0.006 ± 0.001 0.008 ± 0.001 in0.15-0.4 Hz 4. Mean of pulse duration (T_(c)) in 0.75 ± 0.14 0.84 ± 0.15seconds 5. Standard Deviation (SD) of pulse 0.07 ± 0.05 0.09 ± 0.05duration (T_(c)) 6. Mean of relative crest time (T_(s)/T_(c)) 0.29 ±0.04 0.27 ± 0.03 7. SD of relative crest time (T_(s)/T_(c)) 0.02 ± 0.010.03 ± 0.01 8. Mean of relative diastolic 0.71 ± 0.04 0.73 ± 0.03 time(T_(d)/T_(c)) 9. SD of relative diastolic time 0.03 ± 0.01 0.04 ± 0.02(T_(d)/T_(c)) 10. Mean of time ratio (T_(d)/T_(s)) 2.49 ± 0.49 2.81 ±0.53 11. SD of time ratio time (T_(d)/T_(s)) 0.35 ± 0.25 0.43 ± 0.19

TABLE 2 Ranges of PCG features for CAD and non-CAD subjects. Non-CAD Sl.CAD range range No PCG features Mean ± SD Mean ± SD 1. Spectral powerratio between  0.031 ± 0.017 0.041 ± 0.012 0-100 Hz and 100-150 Hz 2.Spectral centroid 602 ± 83 579 ± 92  3. Spectral roll-off  2902 ± 17542745 ± 1681 4. Spectral flux 118.44 ± 51.23 96.77 ± 48.73 5. Kurtosis oftime windows 21.23 ± 5.1  27.62 ± 8.3  6. Natural entropy 123.33 ± 40.1  92.5 ± 38.96 7. Tsallis entropy 1053 ± 434 887 ± 376

FIG. 7 illustrates a flow diagram of a method 700 for the classificationof the CAD based on metadata and cardiovascular signals, according tosome embodiments of the present disclosure. The method 700 may bedescribed in the general context of computer executable instructions.Generally, computer executable instructions can include routines,programs, objects, components, data structures, procedures, modules,functions, etc., that perform particular functions or implementparticular abstract data types. The method 700 may also be practiced ina distributed computing environment where functions are performed byremote processing devices that are linked through a communicationnetwork. The order in which the method 700 is described is not intendedto be construed as a limitation, and any number of the described methodblocks can be combined in any order to implement the method 700, or analternative method. Furthermore, the method 500 can be implemented inany suitable hardware, software, firmware, or combination thereof.

At 702, the system 200 receives, by the one or more hardware processors,a set of metadata associated with the subject and the set ofcardiovascular signal associated with the subject, wherein each metadatafrom the set of metadata is associated with a set of values. At 704, thesystem 200 selects, by the one or more hardware processors, the set ofoptimal parameters from the set of metadata based on a difference infrequency of occurrence of CAD among the disease population and thenon-disease population, wherein the difference in frequency ofoccurrence of CAD among the disease population and the non-diseasepopulation is greater than a predefined threshold. At 706, the system200 computes, by the one or more hardware processors, the optimalthreshold for each optimal parameter from the set of optimal parametersbased on the inflexion point based correlation analysis. At 708, thesystem 200 classifies, by the one or more hardware processors, thesubject by utilizing the metadata based rule engine into a categoryamong a plurality of categories, wherein the plurality of categoriescomprising the confirmed CAD subject, the CAD subject and the non-CADsubject, wherein the metadata based rule engine is constructed byutilizing the set of optimal parameters and the optimal thresholdassociated with each optimal parameter. At 710, the system 200reclassifies, by the one or more hardware processors, the CAD subjectinto one of the confirmed CAD subject and the non-CAD subject based on acombination of the PPG signal classifier and the PCG signal classifierin parallel.

FIG. 8 illustrates a flow diagram for identifying an optimal thresholdfor each metadata from a set of metadata based on the inflexion pointbased correlation analysis, according to some embodiments of the presentdisclosure. At step 802, the set of correlation coefficient for eachvalue associated with each optimal parameter from the set of optimalparameters based on Bhattacharya distance between a histogram associatedwith the disease population and a histogram associated with thenon-disease population is calculated. At step 804, the two dimensionalchart for each optimal parameter from the set of optimal parameters,wherein, the set of values associated with each optimal parameter isplotted along the X plane and the set of correlation coefficientsassociated with each optimal parameter is plotted along the Y plane isplotted. At step 806, the optimal threshold for each optimal parameteris identified by selecting a highest inflexion point from the set ofpoints associated with the two dimensional chart corresponding to eachoptimal parameter from the set of optimal parameters.

FIG. 9 illustrates a flow diagram for reclassifying the CAD subjectbased on the combination of the PPG signal classifier and the PCG signalclassifier in parallel, according to some embodiments of the presentdisclosure. At step 902, the set of cardiovascular signal associatedwith the CAD subject are received, wherein the set of cardiovascularsignal comprises the PPG signal and the PCG signal. At step 904, the setof PPG signal parameters from the PPG signal and the set of PCG signalparameters from the PCG signal are extracted in parallel. Here, the setof PPG signal parameters includes the cycle duration, the systolicupstroke time, the diastolic time, the trough to notch time, the notchto trough time, the peak to notch time and the pulse width. Here, theset of PCG signal parameters includes the spectral power ratio, thespectral centroid, the spectral roll-off, the spectral flux, thekurtosis of time windows, the natural entropy, and the Tsallis entropy.At step 906, the first confidence score and the second confidence scoreare calculated in parallel, wherein the first confidence score isobtained by utilizing the PPG signal classifier and the secondconfidence score is obtained by utilizing the PCG signal classifier.Here, the PPG signal classifier is trained by utilizing a PPG signaltraining data, wherein, the PPG signal training data includes the set ofPPG signal parameters associated with the disease population and the setof PPG signal parameters associated with the non-disease population.Here, the PCG signal classifier is trained by utilizing a PCG signaltraining data, wherein, the PCG signal training data includes the set ofPCG signal parameters associated with the disease population and the setof PCG signal parameters associated with the non-disease population. Atstep 908, the CAD subject is reclassified into one of the confirmed CADsubject and the non-CAD subject by selecting a highest confidence scoreamong the first confidence score and the second confidence score.

In an embodiment, the system 200 is experimented with a datasetcomprising a total of 99 subjects. The data set is collected from anurban hospital in India in a balanced ratio of the CAD and the non-CADsubjects, covering diverse patient demography and medical history. Here,a 5-fold cross validation approach is applied on the entire dataset. Thesubjects classified as the CAD subjects by the metadata based ruleengine are sent to the second stage of classification for decisionmaking. The metadata information are typically obtained based onquestionnaires to the users. Average values of sensitivity andspecificity of classifying the CAD subjects across all the 5 folds arereported as evaluation metrics in the present disclosure. A very highsensitivity is a major requirement for any medical screening system. Onthe other hand, specificity should also be sufficiently high to reducethe false positive rate in disease detection, which is important forusability purpose. Hence, there is a necessity to achieve high values inboth sensitivity and specificity simultaneously, rather than focusing onoverall classification accuracy. FIG. 10 illustrates a comparative studyof individual PCG and PPG classifiers, metadata based rule engine,fusion classifier and the two-stage classifier in terms of meansensitivity and specificity, according to some embodiments of thepresent disclosure. The results indicates that the classifiers designedusing a single cardiovascular signal is not sufficient for very accuratedisease detection. A promising accuracy with balanced sensitivity andspecificity can be achieved by applying the second stage ofclassification on the entire dataset based on fusion of PPG and PCGclassifiers. Here both sensitivity and specificity values reach close to0.8. However, for many of the borderline CAD subjects having 30% orlesser heart blockage, discriminative markers are not always present inshort recordings of cardiovascular signals. This restricts the overallsensitivity and specificity of the system. The metadata based ruleengine, yields a very high sensitivity of 0.96 in detecting the CADsubjects. However specificity drops to 0.77 as the rule engine is biasedtowards the CAD. The proposed two-stage classification technique triesto rectify the misclassification error of the metadata based rule engineby utilizing the cardiovascular signal analysis at second stage forimproving the overall specificity of the system to 0.9, minimallyaffecting the sensitivity (0.92).

The written description describes the subject matter herein to enableany person skilled in the art to make and use the embodiments. The scopeof the subject matter embodiments is defined by the claims and mayinclude other modifications that occur to those skilled in the art. Suchother modifications are intended to be within the scope of the claims ifthey have similar elements that do not differ from the literal languageof the claims or if they include equivalent elements with insubstantialdifferences from the literal language of the claims.

Various embodiments disclosed methods and system for classification ofCAD based on metadata and cardiovascular signals are able to provide anend-to-end solution for accurate classification of the CAD. Thecombination of the metadata based rule engine utilized in the firststage of classification and the cardiovascular signal basedreclassification in the second stage increased the accuracy of thesystem 200. Further, the system 200 classifies the subjects based on theset of demographic data, the set of clinical information and the set ofcardiovascular signals. Moreover, the optimal threshold associated witheach optimal parameter is calculated based on the inflexion basedcorrelation analysis and hence the number of false positives and thenumber of false negatives can be reduced.

It is, however to be understood that the scope of the protection isextended to such a program and in addition to a computer-readable meanshaving a message therein; such computer-readable storage means containprogram-code means for implementation of one or more steps of themethod, when the program runs on a server or mobile device or anysuitable programmable device. The hardware device can be any kind ofdevice which can be programmed including e.g. any kind of computer likea server or a personal computer, or the like, or any combinationthereof. The device may also include means which could be e.g. hardwaremeans like e.g. an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or a combination of hardware andsoftware means, e.g. an ASIC and an FPGA, or at least one microprocessorand at least one memory with software modules located therein. Thus, themeans can include both hardware means and software means. The methodembodiments described herein could be implemented in hardware andsoftware. The device may also include software means. Alternatively, theembodiments may be implemented on different hardware devices, e.g. usinga plurality of CPUs.

The embodiments herein can comprise hardware and software elements. Theembodiments that are implemented in software include but are not limitedto, firmware, resident software, microcode, etc. The functions performedby various modules described herein may be implemented in other modulesor combinations of other modules. For the purposes of this description,a computer-usable or computer readable medium can be any apparatus thatcan comprise, store, communicate, propagate, or transport the programfor use by or in connection with the instruction execution system,apparatus, or device.

The illustrated steps are set out to explain the exemplary embodimentsshown, and it should be anticipated that ongoing technologicaldevelopment will change the manner in which particular functions areperformed. These examples are presented herein for purposes ofillustration, and not limitation. Further, the boundaries of thefunctional building blocks have been arbitrarily defined herein for theconvenience of the description. Alternative boundaries can be defined solong as the specified functions and relationships thereof areappropriately performed. Alternatives (including equivalents,extensions, variations, deviations, etc., of those described herein)will be apparent to persons skilled in the relevant art(s) based on theteachings contained herein. Such alternatives fall within the scope andspirit of the disclosed embodiments. Also, the words “comprising,”“having,” “containing,” and “including,” and other similar forms areintended to be equivalent in meaning and be open ended in that an itemor items following any one of these words is not meant to be anexhaustive listing of such item or items, or meant to be limited to onlythe listed item or items. It must also be noted that as used herein andin the appended claims, the singular forms “a,” “an,” and “the” includeplural references unless the context clearly dictates otherwise.

Furthermore, one or more computer-readable storage media may be utilizedin implementing embodiments consistent with the present disclosure. Acomputer-readable storage medium refers to any type of physical memoryon which information or data readable by a processor may be stored.Thus, a computer-readable storage medium may store instructions forexecution by one or more processors, including instructions for causingthe processor(s) to perform steps or stages consistent with theembodiments described herein. The term “computer-readable medium” shouldbe understood to include tangible items and exclude carrier waves andtransient signals, i.e., be non-transitory. Examples include randomaccess memory (RAM), read-only memory (ROM), volatile memory,nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, andany other known physical storage media.

It is intended that the disclosure and examples be considered asexemplary only, with a true scope and spirit of disclosed embodimentsbeing indicated by the following claims.

What is claimed is:
 1. A method for classification of Coronary ArteryDisease (CAD) based on metadata and cardiovascular signals, the methodcomprising: receiving, by a one or more hardware processors, a set ofmetadata associated with a subject and a set of cardiovascular signalsassociated with the subject, wherein each metadata from the set of metadata is associated with a value; selecting, by the one or more hardwareprocessors, a set of optimal parameters from the set of metadata basedon a difference in frequency of occurrence of CAD among a diseasepopulation and a non-disease population; computing, by the one or morehardware processors, an optimal threshold for each optimal parameterfrom the set of optimal parameters based on an inflexion point basedcorrelation analysis; classifying, by the one or more hardwareprocessors, the subject by utilizing a metadata based rule engine into acategory among a plurality of categories, wherein the plurality ofcategories comprising a confirmed CAD subject, a CAD subject and anon-CAD subject, wherein the metadata based rule engine is constructedby utilizing the set of optimal parameters and the optimal thresholdassociated with each optimal parameter; and reclassifying, by the one ormore hardware processors, the CAD subject into one of the confirmed CADsubject and the non-CAD subject based on a combination of a PPG signalclassifier and a PCG signal classifier in parallel.
 2. The method asclaimed in claim 1, wherein identifying, the optimal threshold for eachoptimal parameter from the set of optimal parameters based on theinflexion point based correlation analysis further comprising:calculating a set of correlation coefficient for each value associatedwith each optimal parameter from the set of optimal parameters based onBhattacharya distance between a histogram associated with the diseasepopulation and a histogram associated with the non-disease population;plotting a two dimensional chart for each optimal parameter from the setof optimal parameters, wherein, the set of values associated with eachoptimal parameter is plotted along an X plane and the set of correlationcoefficients associated with each optimal parameter is plotted along a Yplane; and identifying the optimal threshold for each optimal parameterby selecting a highest inflexion point from a set of points associatedwith the two dimensional chart corresponding to each optimal parameterfrom the set of optimal parameters.
 3. The method as claimed in claim 1,wherein reclassifying, the CAD subject into one of the confirmed CADsubject and the non-CAD subject based on the combination of the PPGsignal classifier and the PCG signal classifier in parallel furthercomprising: receiving the set of cardiovascular signal associated withthe CAD subject, wherein the set of cardiovascular signal comprises aPPG signal and a PCG signal; extracting a set of PPG signal parametersfrom the PPG signal and a set of PCG signal parameters from the PCGsignal in parallel; calculate a first confidence score and a secondconfidence score in parallel, wherein the first confidence score isobtained by utilizing the PPG signal classifier and the secondconfidence score is obtained by utilizing the PCG signal classifier; andreclassifying the CAD subject into one of the confirmed CAD subject andthe non-CAD subject by selecting a highest confidence score among thefirst confidence score and the second confidence score.
 4. The method asclaimed in claim 1, wherein the set of metadata comprising a set ofdemographic data and a set of clinical information.
 5. The method asclaimed in claim 3, wherein the set of PCG signal parameters comprises aspectral power ratio, a spectral centroid, a spectral roll-off, aspectral flux, a kurtosis of time windows, a natural entropy, and aTsallis entropy.
 6. The method as claimed in claim 3, wherein the set ofPPG signal parameters comprises a cycle duration, a systolic upstroketime, a diastolic time, a trough to notch time, a notch to trough time,a peak to notch time and a pulse width.
 7. The method as claimed inclaim 3, wherein the PPG signal classifier is trained by utilizing a PPGsignal training data, wherein, the PPG signal training data includes theset of PPG signal parameters associated with the disease population andthe set of PPG signal parameters associated with the non-diseasepopulation.
 8. The method as claimed in claim 3, wherein the PCG signalclassifier is trained by utilizing a PCG signal training data, wherein,the PCG signal training data includes the set of PCG signal parametersassociated with the disease population and the set of PCG signalparameters associated with the non-disease population.
 9. A system forclassification of Coronary Artery Disease (CAD) based on metadata andcardiovascular signals, the system comprising: one or more memoriescomprising programmed instructions and a repository for storing a set ofmetadata associated with a subject and a set of cardiovascular signalassociated with the subject; one or more hardware processors operativelycoupled to the one or more memories, wherein the one or more hardwareprocessors are capable of executing the programmed instructions storedin the one or more memories; a PPG signal capturing device, a PCG signalcapturing device and a CAD analysis unit, wherein the CAD analysis unitis configured to: receive, a set of metadata associated with a subjectand a set of cardiovascular signal associated with the subject, whereineach metadata from the set of meta data is associated with a value;select, a set of optimal parameters from the set of metadata based on adifference in frequency of occurrence of CAD among a disease populationand a non-disease population; compute, an optimal threshold for eachoptimal parameter from the set of optimal parameters based on aninflexion point based correlation analysis; classify, the subject byutilizing a metadata based rule engine into a category among a pluralityof categories, wherein the plurality of categories comprising aconfirmed CAD subject, a CAD subject and a non-CAD subject, wherein themetadata based rule engine is constructed by utilizing the set ofoptimal parameters and the optimal threshold associated with eachoptimal parameter; and reclassify, the CAD subject into one of theconfirmed CAD subject and the non-CAD subject based on a combination ofa PPG signal classifier and a PCG signal classifier in parallel.
 10. Thesystem as claimed in claim 9, wherein the CAD analysis unit isconfigured to identify the optimal threshold for each optimal parameterfrom the set of optimal parameters based on the inflexion point basedcorrelation analysis by: calculating a set of correlation coefficientfor each value associated with each optimal parameter from the set ofoptimal parameters based on Bhattacharya distance between a histogramassociated with the disease population and a histogram associated withthe non-disease population; plotting a two dimensional chart for eachoptimal parameter from the set of optimal parameters, wherein, the setof values associated with each optimal parameter is plotted along an Xplane and the set of correlation coefficients associated with eachoptimal parameter is plotted along a Y plane; and identifying theoptimal threshold for each optimal parameter by selecting a highestinflexion point from a set of points associated with the two dimensionalchart corresponding to each optimal parameter from the set of optimalparameters.
 11. The system as claimed in claim 9, wherein the CADanalysis unit is configured to reclassify the CAD subject into one ofthe confirmed CAD subject and the non-CAD subject based on thecombination of the PPG signal classifier and the PCG signal classifierin parallel by: receiving the set of cardiovascular signal associatedwith the CAD subject, wherein the set of cardiovascular signal comprisesa PPG signal and a PCG signal; extracting a set of PPG signal parametersfrom the PPG signal and a set of PCG signal parameters from the PCGsignal in parallel; calculate a first confidence score and a secondconfidence score in parallel, wherein the first confidence score isobtained by utilizing the PPG signal classifier and the secondconfidence score is obtained by utilizing the PCG signal classifier; andreclassifying the CAD subject into one of the confirmed CAD subject andthe non-CAD subject by selecting a highest confidence score among thefirst confidence score and the second confidence score.
 12. The systemas claimed in claim 9, wherein the set of metadata comprising a set ofdemographic data and a set of clinical information.
 13. The system asclaimed in claim 11, wherein the set of PCG signal parameters comprisesa spectral power ratio, a spectral centroid, a spectral roll-off, aspectral flux, a kurtosis of time windows, a natural entropy, and aTsallis entropy.
 14. The system as claimed in claim 11, wherein the setof PPG signal parameters comprises a cycle duration, a systolic upstroketime, a diastolic time, a trough to notch time, a notch to trough time,a peak to notch time and a pulse width.
 15. The system as claimed inclaim 11, wherein the PPG signal classifier is trained by utilizing aPPG signal training data, wherein, the PPG signal training data includesthe set of PPG signal parameters associated with the disease populationand the set of PPG signal parameters associated with the non-diseasepopulation.
 16. The system as claimed in claim 11, wherein the PCGsignal classifier is trained by utilizing a PCG signal training data,wherein, the PCG signal training data includes the set of PCG signalparameters associated with the disease population and the set of PCGsignal parameters associated with the non-disease population.
 17. One ormore non-transitory machine readable information storage mediumscomprising one or more instructions which when executed by one or morehardware processors causes the one or more hardware processor to performa method for classification of Coronary Artery Disease (CAD) based onmetadata and cardiovascular signals, said method comprising: receiving,by the one or more hardware processors, a set of metadata associatedwith a subject and a set of cardiovascular signals associated with thesubject, wherein each metadata from the set of meta data is associatedwith a value; selecting, by the one or more hardware processors, a setof optimal parameters from the set of metadata based on a difference infrequency of occurrence of CAD among a disease population and anon-disease population; computing, by the one or more hardwareprocessors, an optimal threshold for each optimal parameter from the setof optimal parameters based on an inflexion point based correlationanalysis; classifying, by the one or more hardware processors, thesubject by utilizing a metadata based rule engine into a category amonga plurality of categories, wherein the plurality of categoriescomprising a confirmed CAD subject, a CAD subject and a non-CAD subject,wherein the metadata based rule engine is constructed by utilizing theset of optimal parameters and the optimal threshold associated with eachoptimal parameter; and reclassifying, by the one or more hardwareprocessors, the CAD subject into one of the confirmed CAD subject andthe non-CAD subject based on a combination of a PPG signal classifierand a PCG signal classifier in parallel.
 18. The one or morenon-transitory machine readable information storage mediums of claim 17,wherein identifying, the optimal threshold for each optimal parameterfrom the set of optimal parameters based on the inflexion point basedcorrelation analysis further comprising: calculating a set ofcorrelation coefficient for each value associated with each optimalparameter from the set of optimal parameters based on Bhattacharyadistance between a histogram associated with the disease population anda histogram associated with the non-disease population; plotting a twodimensional chart for each optimal parameter from the set of optimalparameters, wherein, the set of values associated with each optimalparameter is plotted along an X plane and the set of correlationcoefficients associated with each optimal parameter is plotted along a Yplane; and identifying the optimal threshold for each optimal parameterby selecting a highest inflexion point from a set of points associatedwith the two dimensional chart corresponding to each optimal parameterfrom the set of optimal parameters.
 19. The one or more non-transitorymachine readable information storage mediums of claim 17, whereinreclassifying, the CAD subject into one of the confirmed CAD subject andthe non-CAD subject based on the combination of the PPG signalclassifier and the PCG signal classifier in parallel further comprising:receiving the set of cardiovascular signal associated with the CADsubject, wherein the set of cardiovascular signal comprises a PPG signaland a PCG signal; extracting a set of PPG signal parameters from the PPGsignal and a set of PCG signal parameters from the PCG signal inparallel; calculate a first confidence score and a second confidencescore in parallel, wherein the first confidence score is obtained byutilizing the PPG signal classifier and the second confidence score isobtained by utilizing the PCG signal classifier; and reclassifying theCAD subject into one of the confirmed CAD subject and the non-CADsubject by selecting a highest confidence score among the firstconfidence score and the second confidence score.
 20. The one or morenon-transitory machine readable information storage mediums of claim 17,wherein the set of metadata comprising a set of demographic data and aset of clinical information.